The invention relates to the automatic diagnosis of networks of components.
A diagnosis device capable of operating on analogue signals is described in an European Application (EP-A-408 425). It works on the basis of functional models. These comprise component-expressions pertaining to physical quantities relating to a component, such as Ohm's law (V=I.R); they also comprise law-expressions representing general relations between physical quantities, such as the Kirchhoff's first law; the algebraic sum of the currents at a node is zero.
Diagnosis begins from acquired physical quantities. These are stored in memory in the form of samples, matched up with a corresponding specification of the relevant nodes and components of the network.
If the device works "blind" (without knowing a priori the layout of the network), the specification of the nodes and components must be sufficient to allow it to learn the layout of the network along with its exploration of the latter. If, on the contrary, the layout of the network is known in advance, it is sufficient to designate each measurement point in this known layout.
For each acquisition, the samples are referred to a chosen working time interval. The device searches therein for anomalies with respect to the functional models (law-expressions and component-expressions). This is done for each sampling instant in the working time interval.
Generally, the samples of physical quantities acquired do not lend themselves directly to the detection of anomalies. First, it is necessary to compute estimated physical quantities, as many as necessary so as no longer to have any unknowns. A violated expression then makes it possible in principle, bearing in mind the uncertainties, to locate the fault.
The aim is to locate a defective component in the relevant network and to do so in minimum time.
However, it has transpired that difficulties persisted for certain applications.
First, the prior device functions by "propagation": for each sampling instant it is necessary, starting from the measured samples, to compute as many estimated quantities as necessary, and then to work iteratively on the models until the faulty component is found. It is clear that the number of operations, and consequently the time elapsed, grow very quickly, as the complexity of the circuit to be analysed increases.
Moreover, to produce the functional models it is necessary to "bracket" certain variables, that is to say fix a minimum and a maximum in respect thereof. Now, in certain situations, analogue signals (voltage or current) vary very rapidly. It transpired that bracketing their derivatives then becomes particularly tricky, especially when uncertainty is taken into account.
These difficulties are made worse by the fact that, in most cases, there is furthermore reason to consider uncertainty ranges rather than raw values.
They are made worse also each time that it is necessary to bring into the models not only the quantities themselves, but also their first derivatives (or higher-order derivatives), as is often the case. It is often difficult to bracket such derivatives by uncertainty ranges, in the presence of fast variations in the basic quantities themselves.